Instance-Conditioned GAN (2109.05070v2)

Abstract: Generative Adversarial Networks (GANs) can generate near photo realistic images in narrow domains such as human faces. Yet, modeling complex distributions of datasets such as ImageNet and COCO-Stuff remains challenging in unconditional settings. In this paper, we take inspiration from kernel density estimation techniques and introduce a non-parametric approach to modeling distributions of complex datasets. We partition the data manifold into a mixture of overlapping neighborhoods described by a datapoint and its nearest neighbors, and introduce a model, called instance-conditioned GAN (IC-GAN), which learns the distribution around each datapoint. Experimental results on ImageNet and COCO-Stuff show that IC-GAN significantly improves over unconditional models and unsupervised data partitioning baselines. Moreover, we show that IC-GAN can effortlessly transfer to datasets not seen during training by simply changing the conditioning instances, and still generate realistic images. Finally, we extend IC-GAN to the class-conditional case and show semantically controllable generation and competitive quantitative results on ImageNet; while improving over BigGAN on ImageNet-LT. Code and trained models to reproduce the reported results are available at https://github.com/facebookresearch/ic_gan.

• The paper introduces a novel instance conditioning mechanism using local density (KDE) to enhance image synthesis quality.
• It applies a non-parametric approach with instance-specific features, leading to improved FID and Inception Scores on benchmarks like ImageNet.
• The model demonstrates robust transferability across datasets such as COCO-Stuff and ImageNet-LT, showcasing its effectiveness on imbalanced data.

Instance-Conditioned GAN: A Precise Advancement in Image Generation

The paper "Instance-Conditioned GAN (IC-GAN)" presents a refined approach to Generative Adversarial Networks (GANs) in the domain of realistic image synthesis. The authors introduce a novel model that leverages instance conditioning, significantly enhancing the quality and diversity of generated images without requiring labeled data.

Key Contributions

IC-GAN reshapes the traditional GAN framework by conditioning both the generator and the discriminator on instance-specific features. This instance conditioning is informed by the concept of kernel density estimation (KDE), where the model focuses on the distribution around a specific datapoint and its nearest neighbors. This local conditioning strategy allows IC-GAN to maintain high-quality output across complex datasets, such as ImageNet and COCO-Stuff, which have traditionally posed challenges for unconditional GANs.

Methodology and Implementation

The core idea behind IC-GAN is straightforward yet impactful: the model utilizes a non-parametric approach to model the data distribution. It achieves this by leveraging a mixture of local densities, conditioned on the rich feature embeddings of individual instances and their nearest neighbors. The coupling of the generator and discriminator with these instance features naturally encourages the generation of images that are similar to the neighborhoods of their conditioning instances.

The model's reliance on instance features, instead of labeled data, enables it to generalize well, even to datasets not encountered during training. Such a generalization capacity is validated through transfer experiments where IC-GAN demonstrates robust performance across a variety of unseen datasets.

Empirical Results and Comparative Analysis

IC-GAN demonstrates superior results across multiple benchmarks:

• On ImageNet, IC-GAN outperforms existing methods in both the unlabeled and class-conditional paradigms, demonstrating lower FID scores and higher Inception Scores, indicative of enhanced visual quality and diversity.
• In the transfer experiments, the model trained on ImageNet achieved state-of-the-art performance on COCO-Stuff, even without direct training data from the latter dataset. This aspect underlines the adaptability and robustness of the model's structure.
• With class-conditional tasks on ImageNet-LT (a long-tail distribution), IC-GAN exhibited improved performance for every class density category, showcasing its ability to handle imbalanced data effectively.

Implications and Future Directions

Practically, the instance-conditioning mechanism allows for semantically controllable image generation. This provides users with the ability to adjust style and semantics in image synthesis, potentially impacting creative fields and expanding the utility of GANs in applications requiring high adaptability and minimal supervision.

Theoretically, IC-GAN offers insights into the advantages of incorporating non-parametric density estimation techniques within adversarial frameworks. The apparent success of IC-GAN in handling complex distributions points towards future enhancements in generative models that blend traditional statistical methods with deep learning architectures.

Conclusion

IC-GAN represents a significant step towards improved image generation models that are both high-performing and flexible across a variety of conditions and datasets. While the model showcases strengths in diverse scenarios, its reliance on robust feature extraction highlights future avenues for integration and optimization in feature representation learning. As the field progresses, IC-GAN’s blend of instance-conditioning, alongside its architectural innovations, may well inspire further advancements in generative modeling techniques.